Advanced Driver Assistance Systems (ADAS) with proactive alerts have been used to increase driving safety. Such systems’ performance greatly depends on how accurately and quickly the risky situations and maneuvers are detected. Existing ADAS provide warnings based on the vehicle’s operational status, detection of environments, and the drivers’ overt actions (e.g., using turn signals or steering wheels), which may not give drivers as much as optimal time to react. In this paper, we proposed a spatio-temporal attention-based neural network to predict drivers’ lane-change intention by fusing the videos from both in-cabin and forward perspectives. The Convolutional Neural Network (CNN)-Recursive Neural Network (RNN) network architecture was leveraged to extract both the spatial and temporal information. On top of this network backbone structure, the feature maps from different time steps and perspectives were fused using multi-head self-attention at each resolution of the CNN. The proposed model was trained and evaluated using a processed subset of the MIT Advanced Vehicle Technology (MIT-AVT) dataset which contains synchronized CAN data, 11058-second videos from 3 different views, 548 lane-change events, and 274 non-lane-change events performed by 83 drivers. The results demonstrate that the model achieves 87% F1-score within the 1-second validation window and 70% F1-score within the 5-second validation window with real-time performance.

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane-change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched athttps://youtu.be/5cbsabgIOdM.

In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when human drivers bring in a wide range of uncertainties. In this paper, we propose a learning-based lane-change prediction algorithm that considers the driving behaviors of the target human driver. To provide accurate maneuver prediction, we adopt a hierarchical structure that seamlessly seals both the lane-change decision prediction and the vehicle trajectory pre-diction together. Specifically, we propose a lane-change decision prediction method based on a Long-Short Term Memory (LSTM) network, and a trajectories prediction considering driver preference and vehicular interactions based on Inverse Reinforcement Learning (IRL). To validate the performance of the proposed methodology, a case study of an on-ramp merging scenario is conducted on a uniquely built human-in-the-loop simulation platform that can provide an immersive driving environment, collect data of lane-change behaviors, and test drivers' reactions to the prediction results in real time. It is shown in the simulation results that we can predict the lane-change decision 3 seconds before the vehicle crosses the line to another lane, and the Mean Euclidean Distance between the predicted trajectory and ground truth is 0.39 meters within a 4-second prediction window.

Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert systems or prede-fined control strategies are not in line with individual human driver's preference. To overcome this problem, we propose a Personalized Adaptive Cruise Control (P-ACC) system that can learn the driver's car-following preferences from historical data using model-based maximum entropy Inverse Reinforcement Learning (IRL). Once activated in real-time, the P-ACC system first classifies the driver type and the weather type (at that moment). The vehicle is then controlled using the pre-trained IRL model on the cloud of the associated class. The personalized IRL model on the cloud will be updated as more human driving data is collected from various scenarios. Numerical simulation with real-world naturalistic driving data shows that, the accuracy of reproducing the real-world driving profile improves up to 30.1% in terms of speed and 36.5% in terms of distance gap, when P-ACC is compared with the Intelligent Driver Model (IDM). Game engine-based human-in-the-loop simulation demonstrates that, the takeover frequency of the driver during the usage of P-ACC decreases up to 93.4%, compared with that during the usage of IDM-based ACC.

Our current transportation system suffers from a number of problems in terms of safety, mobility, and environmental sustainability. The emergence of innovative intelligent transportation systems (ITS) technologies, and in particular connected and automated vehicles (CAVs), provides many opportunities to address the aforementioned issues. In this paper, we propose a hierarchical ramp merging system that not only generates microscopic cooperative maneuvers for CAVs on the ramp to merge into the mainline traffic flow, but also provides controllability of the ramp inflow rate, thereby enabling macroscopic traffic flow control. A centralized optimal control-based approach is proposed to smooth the merging flow, improve the system-wide mobility, and decrease the overall fuel consumption of the network. Linear quadratic trackers in both finite horizon and receding horizon forms are developed to solve the optimization problem in terms of path planning and sequence determination, where a microscopic vehicle fuel consumption model is applied. Extensive traffic simulation runs have been conducted using PTV VISSIM to evaluate the impact of the proposed system on a segment of SR-91 E in Corona, California. The results confirm that under the regulated inflow rate, the proposed system can avoid potential traffic congestion and improve mobility (e.g., VMT/VHT) up to 147%, with a 47% fuel savings compared to the conventional ramp metering and the ramp without any control approach.